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1. How does sustaining and interleaving visual scaffolding help learners? a classroom study with an intelligent tutoring system

   Nagashima, T. ; Ling, E. ; Zheng, B. ; Bartel, A. N. ; Silla, E. M. ; Vest, N. A. ; Alibali, M. W. ; Aleven, V. ( July 2022 , Proceedings of the 44th Annual Conference of the Cognitive Science Society)

   Culbertson, J. ; Perfors, A. ; Rabagliati, H. ; Ramenzoni, V. (Ed.)

   Integrating visual representations in an interactive learning activity effectively scaffolds performance and learning. However, it is unclear whether and how sustaining or interleaving visual scaffolding helps learners solve problems efficiently and learn from problem solving. We conducted a classroom study with 63 middle-school students in which we tested whether sustaining or interleaving a particular form of visual scaffolding, called anticipatory diagrammatic self-explanation in an Intelligent Tutoring System, helps students’ learning and performance in the domain of early algebra. Sustaining visual scaffolding during problem solving helped students solve problems efficiently with no negative effects on learning. However, in-depth log data analyses suggest that interleaving visual scaffolding allowed students to practice important skills that may help them in later phases of algebra learning. This paper extends scientific understanding that sustaining visual scaffold does not over-scaffold student learning in the early phase of skill acquisition in algebra. 

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2. Scaffolded self-explanation with visual representations promotes efficient learning in early algebra

   https://doi.org/10.31219/osf.io/sbwfj  

   Nagashima, T. ; Bartel, A. N. ; Tseng, S. ; Vest, N. A. ; Silla, E. M. ; Alibali, M. W. ; Aleven, V. ( January 2021 , Proceedings of the 43rd Annual Conference of the Cognitive Science Society)

   Fitch, T. ; Lamm, C. ; Leder, H. ; Teßmar-Raible, K. (Ed.)

   Although visual representations are generally beneficial for learners, past research also suggests that often only a subset of learners benefits from visual representations. In this work, we designed and evaluated anticipatory diagrammatic self-explanation, a novel form of instructional scaffolding in which visual representations are used to guide learners’ inference generation as they solve algebra problems in an Intelligent Tutoring System. We conducted a classroom experiment with 84 students in grades 5-8 in the US to investigate the effectiveness of anticipatory diagrammatic self-explanation on algebra performance and learning. The results show that anticipatory diagrammatic self-explanation benefits learners on problem-solving performance and the acquisition of formal problem-solving strategies. These effects mostly did not depend on students’ prior knowledge. We analyze and discuss how performance with the visual representation may have influenced the enhanced problem-solving performance. 

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3. Using Adaptive Parsons Problems to Scaffold Write-Code Problems

   https://doi.org/10.1145/3501385.3543977  

   Hou, Xinying ; Ericson, Barbara Jane ; Wang, Xu ( August 2022 , Proceedings of the 2022 ACM Conference on International Computing Education Research)

   In this paper, we explore using Parsons problems to scaffold novice programmers who are struggling while solving write-code problems. Parsons problems, in which students put mixed-up code blocks in order, can be created quickly and already serve thousands of students while other types of programming support methods are expensive to develop or do not scale. We conducted two studies in which novices were given equivalent Parsons problems as optional scaffolding while solving write-code problems. We investigated when, why, and how students used the Parsons problems as well as their perceptions of the benefits and challenges. A think-aloud observational study with 11 undergraduate students showed that students utilized the Parsons problem before writing a solution to get ideas about where to start; during writing a solution when they were stuck; and after writing a solution to debug errors and look for better strategies. Semi-structured interviews with the same 11 undergraduate students provided evidence that using Parsons problems to scaffold write-code problems helped students to reduce the difficulty, reduce the problem completion time, learn problem-solving strategies, and refine their programming knowledge. However, some students found them less useful if the Parsons solution did not match their approach or if they did not understand the solution. We then conducted a between-subjects classroom study with 81 undergraduate students to investigate the effects on learning. We found that students who received Parsons problems as scaffolding during write-code problems spent significantly less time solving those problems. However, there was no significant learning gain in either condition from pretest to posttest. We also discuss the design implications of our findings. 

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4. Using anticipatory diagrammatic self-explanation to support learning and performance in early algebra.

   Nagashima, T. ; Bartel, A. N. ; Yadav, G. ; Tseng, S. ; Vest, N. A. ; Silla, E. M. ; Alibali, M. W. ; Aleven, V. A. ( January 2021 , Annual Meeting of the International Society of the Learning Sciences–ISLS 2021)

   de Vries, E. ; Ahn, J. ; Hod, Y. (Ed.)

   Prior research shows that self-explanation promotes understanding by helping learners connect new knowledge with prior knowledge. However, despite ample evidence supporting the effectiveness of self-explanation, an instructional design challenge emerges in how best to scaffold self-explanation. In particular, it is an open challenge to design self-explanation support that simultaneously facilitates performance and learning outcomes. Towards this goal, we designed anticipatory diagrammatic self-explanation, a novel form of self-explanation embedded in an Intelligent Tutoring System (ITS). In our ITS, anticipatory diagrammatic self-explanation scaffolds learners by providing visual representations to help learners predict an upcoming strategic step in algebra problem solving. A classroom experiment with 108 middle-school students found that anticipatory diagrammatic self-explanation helped students learn formal algebraic strategies and significantly improve their problem-solving performance. This study contributes to understanding of how self-explanation can be scaffolded to support learning and performance. 

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5. Using anticipatory diagrammatic self-explanation to support learning and performance in early algebra

   https://doi.org/10.22318/icls2021.474  

   Nagashima, T. ; Bartel, A. N. ; Yadav, G. ; Tseng, S. ; Vest, N. A. ; Silla, E. M. ; Alibali, M. W. ; Aleven, V. ( January 2021 , Annual Meeting of the International Society of the Learning Sciences–ISLS 2021.)

   de Vries, E. ; Ahn, J. ; Y. Hod, Y. (Ed.)

   Prior research shows that self-explanation promotes understanding by helping learners connect new knowledge with prior knowledge. However, despite ample evidence supporting the effectiveness of self-explanation, an instructional design challenge emerges in how best to scaffold self-explanation. In particular, it is an open challenge to design self-explanation support that simultaneously facilitates performance and learning outcomes. Towards this goal, we designed anticipatory diagrammatic self-explanation, a novel form of self-explanation embedded in an Intelligent Tutoring System (ITS). In our ITS, anticipatory diagrammatic self-explanation scaffolds learners by providing visual representations to help learners predict an upcoming strategic step in algebra problem solving. A classroom experiment with 108 middle-school students found that anticipatory diagrammatic self-explanation helped students learn formal algebraic strategies and significantly improve their problem-solving performance. This study contributes to understanding of how self-explanation can be scaffolded to support learning and performance. 

   more » « less